

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a_      =     {
    'configuration_funnel': ['FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FunnelConfig'],
    'convert_funnel_original_tf_checkpoint_to_pytorch': [],
    'tokenization_funnel': ['FunnelTokenizer'],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_      =     ['FunnelTokenizerFast']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_      =     [
			    'FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'FunnelBaseModel',
			    'FunnelForMaskedLM',
			    'FunnelForMultipleChoice',
			    'FunnelForPreTraining',
			    'FunnelForQuestionAnswering',
			    'FunnelForSequenceClassification',
			    'FunnelForTokenClassification',
			    'FunnelModel',
			    'FunnelPreTrainedModel',
			    'load_tf_weights_in_funnel',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_      =     [
			    'TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'TFFunnelBaseModel',
			    'TFFunnelForMaskedLM',
			    'TFFunnelForMultipleChoice',
			    'TFFunnelForPreTraining',
			    'TFFunnelForQuestionAnswering',
			    'TFFunnelForSequenceClassification',
			    'TFFunnelForTokenClassification',
			    'TFFunnelModel',
			    'TFFunnelPreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
			from .tokenization_funnel import FunnelTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_funnel_fast import FunnelTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_funnel import (
						    FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FunnelBaseModel,
						    FunnelForMaskedLM,
						    FunnelForMultipleChoice,
						    FunnelForPreTraining,
						    FunnelForQuestionAnswering,
						    FunnelForSequenceClassification,
						    FunnelForTokenClassification,
						    FunnelModel,
						    FunnelPreTrainedModel,
						    load_tf_weights_in_funnel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_funnel import (
						    TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFFunnelBaseModel,
						    TFFunnelForMaskedLM,
						    TFFunnelForMultipleChoice,
						    TFFunnelForPreTraining,
						    TFFunnelForQuestionAnswering,
						    TFFunnelForSequenceClassification,
						    TFFunnelForTokenClassification,
						    TFFunnelModel,
						    TFFunnelPreTrainedModel,
						)

else:
			import sys

			a_      =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





"""simple docstring"""




def        a__					(							snake_case__ = 50_00_00_00							)		->   int:
			lowerCamelCase      =   set()
			lowerCamelCase      =   int((limit - 24) ** (1 / 2)							)

			lowerCamelCase      =   set(range(3			,					prime_square_limit + 1			,					2							)							)
			primes.add(2							)
			for p in range(3			,					prime_square_limit + 1			,					2							):
						if p not in primes:
									continue
						primes.difference_update(set(range(p * p			,					prime_square_limit + 1			,					snake_case__							)							)							)

			for primea in primes:
						lowerCamelCase      =   primea * primea
						for primea in primes:
									lowerCamelCase      =   primea * primea * primea
									if square + cube >= limit - 16:
												break
									for primea in primes:
												lowerCamelCase      =   primea * primea * primea * primea
												lowerCamelCase      =   square + cube + tetr
												if total >= limit:
															break
												ret.add(snake_case__							)

			return len(snake_case__							)


if __name__ == "__main__":
					print(F"""{solution() = }""")



